Background:Reinforcing-reducing manipulations are a crucial part of acupuncture therapy.The reinforcing manipulation with heat sensation(RMHS) and the reducing manipulation by inducing cool sensation(RMCS) are two rep...Background:Reinforcing-reducing manipulations are a crucial part of acupuncture therapy.The reinforcing manipulation with heat sensation(RMHS) and the reducing manipulation by inducing cool sensation(RMCS) are two representative methods of reinforcing-reducing manipulations.This trial aims to investigate the characteristic of cerebral responses to these two typical acupuncture manipulations.Methods:A total of 35 healthy participants will be included and receive acupuncture stimulation with RMHS and RMCS in a random order.The psychology,cognition,and body constitution of participants will be measured by the Self-rating Anxiety Scale(SAS),the Self-rating Depression Scale(SDS),the mindful attention awareness scale(MAAS),the Constitution in Chinese Medicine Questionnaire(CCMQ),and the needle sensation will be evaluated after each acupuncture stimulation immediately.The cerebral activity changes elicited by RMHS and RMCS will be detected by functional near-infrared spectroscopy(fNIRS)and analyzed with the general linear model-based activation and the channel-to-channel functional connectivity.Discussion:This trial will investigate the cerebral response induced by RMHS and RMCS by real-time fNIRS,so as to map the characteristics of the central responses’ patterns to these two manipulations and preliminarily explore the mechanism of traditional acupuncture manipulations.展开更多
In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunc...In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.展开更多
The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the sc...The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes. In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shape-adaptive Gaussian kernels are employed to represent the spatial regions of different OBIs. More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges. Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes. Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency. Dataset is available at http://jgw.aynu.edu.cn.展开更多
基金supported by National Natural Science Foundation of China(52202354,52272097,52072369)Open Funding Project of the State Key Laboratory of Biochemical Engineering&Key Laboratory of Biopharmaceutical Preparation and Delivery(2023KF-04)。
基金supported by grants from the National Natural Science Foundation of China (NO.81973960)the Sichuan Scientific and Technological Innovation Team (No.2019JDTD0011)。
文摘Background:Reinforcing-reducing manipulations are a crucial part of acupuncture therapy.The reinforcing manipulation with heat sensation(RMHS) and the reducing manipulation by inducing cool sensation(RMCS) are two representative methods of reinforcing-reducing manipulations.This trial aims to investigate the characteristic of cerebral responses to these two typical acupuncture manipulations.Methods:A total of 35 healthy participants will be included and receive acupuncture stimulation with RMHS and RMCS in a random order.The psychology,cognition,and body constitution of participants will be measured by the Self-rating Anxiety Scale(SAS),the Self-rating Depression Scale(SDS),the mindful attention awareness scale(MAAS),the Constitution in Chinese Medicine Questionnaire(CCMQ),and the needle sensation will be evaluated after each acupuncture stimulation immediately.The cerebral activity changes elicited by RMHS and RMCS will be detected by functional near-infrared spectroscopy(fNIRS)and analyzed with the general linear model-based activation and the channel-to-channel functional connectivity.Discussion:This trial will investigate the cerebral response induced by RMHS and RMCS by real-time fNIRS,so as to map the characteristics of the central responses’ patterns to these two manipulations and preliminarily explore the mechanism of traditional acupuncture manipulations.
文摘In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.
文摘The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes. In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shape-adaptive Gaussian kernels are employed to represent the spatial regions of different OBIs. More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges. Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes. Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency. Dataset is available at http://jgw.aynu.edu.cn.
